Analyzing Control Problems and Improving Control Loop Performance
|
|
- Brice Allen
- 6 years ago
- Views:
Transcription
1 OptiControls Inc. Houston, TX Ph: Analyzing Control s and Improving Control Loop Performance -by Jacques F. Smuts Page: 1
2 Presenter Principal Consultant at OptiControls Inc. League City, TX 20 years experience in process control Loop optimization and troubleshooting Consulting and control strategy design Process control training Senior member of ISA, P.E., Ph.D. Author of the book: Process Control for Practitioners Page: 2
3 Poor Loop Performance Loop is in manual Poor performance in auto Loop oscillates or goes unstable Sluggish and/or large deviations from set point Often not tuning related Sluggishness Large Deviations Process Variable Set Point Oscillations Page: 3
4 Analysis Controller In Manual Poor Performance in Auto Oscillations Random Deviations Externally Internally Cyclical Interaction Measurement Noise Disturbance Set Point Process Fast Disturbances Page: 4
5 Analysis Controller In Manual Poor Performance in Auto Oscillations Random Deviations Externally Internally Cyclical Interaction Measurement Noise Disturbance Set Point Process Fast Disturbances Page: 5
6 Controller in Manual Some loops: Okay to be in manual Redundant, standby equipment Certain process modes All other loops should be in auto Check reason for manual with operator Review historical performance Place in automatic mode to see response Page: 6
7 Analysis Controller In Manual Poor Performance in Auto Oscillations Random Deviations Externally Internally Cyclical Interaction Measurement Noise Disturbance Set Point Process Fast Disturbances Page: 7
8 Cyclical or Random Analyze process variable to determine Visually Use frequency analysis software Oscillations have a repeatable, constant period Noise and disturbances are random Page: 8
9 Analysis Controller In Manual Poor Performance in Auto Oscillations Random Deviations Externally Internally Cyclical Interaction Measurement Noise Disturbance Set Point Process Fast Disturbances Page: 9
10 Internal vs. External Oscillations Diagnostic Test: Place loop in manual PV continues to oscillate external source Oscillation ceases internal problem Page: 10
11 Analysis Controller In Manual Poor Performance in Auto Oscillations Random Deviations Externally Internally Cyclical Interaction Measurement Noise Disturbance Set Point Process Fast Disturbances Page: 11
12 Oscillating Set Point E.g. steam flow loop oscillates and its set point oscillates Place temperature loop in manual Flow loop stops oscillating: analyze temperature loop Flow loop still oscillates: analyze flow loop Steam FT Set point Controller output FC Steam-flow controller Heat exchanger TC Temperature controller TT Page: 12
13 Analysis Controller In Manual Poor Performance in Auto Oscillations Random Deviations Externally Internally Cyclical Interaction Measurement Noise Disturbance Set Point Process Fast Disturbances Page: 13
14 Interaction through Process Interactive process Heat integration, recycle, etc. Oscillating loop elsewhere Affects other loops Interaction analysis tools PAS, ExperTune, Matrikon P&ID and Process Historian Look for leading oscillation Look at shape of oscillation Page: 14
15 Analysis Controller In Manual Poor Performance in Auto Oscillations Random Deviations Externally Internally Cyclical Interaction Measurement Noise Disturbance Set Point Process Fast Disturbances Page: 15
16 Stiction Short for Static Friction Final control element is sticky Can cause a stick-slip cycle with loop in auto Stiction test: small controller output changes Stick-Slip Cycle White water flow controller Page: 16
17 Positioner Defective positioner Incorrectly tuned positioner Sticky valve Revealed through small step tests HP Flare Scrubber Flow Control as found oscillating severely Step tests revealed positioner overshoot After tuning still oscillating because of stiction, but much less Page: 17
18 Nonlinearity Loop unstable under certain conditions Nonlinear valve characteristic Plot Flow vs. % Open Use characterizer Nonlinear process Tune under different conditions Use gain scheduling Process Variable Low gain High gain Steam Pressure Controller Output Page: 18
19 Analysis Controller In Manual Poor Performance in Auto Oscillations Random Deviations Externally Internally Cyclical Interaction Measurement Noise Disturbance Set Point Process Fast Disturbances Page: 19
20 Controller Methods Tune controller according to the process characteristics Tune controllers to meet control objective Trial-and-error tuning rules software Better Results Page: 20
21 Example Oil-Gas Separator Level Control As Found Quick Stabilization Step Tests After With PV Filter Page: 21
22 Analysis Controller In Manual Poor Performance in Auto Oscillations Random Deviations Externally Internally Cyclical Interaction Measurement Noise Disturbance Set Point Process Fast Disturbances Page: 22
23 Cyclical Interaction Loops fighting with each other Continuous, interactive oscillations Strong interaction Similar dynamics Aggressive tuning Page: 23
24 Solutions for Interactive Loops Dynamically separate loop response times Tune most important loop for fast response Tune interacting loop to respond 3 x slower Apply dynamic decoupling Two cross-coupled feedforward controllers between loops Implement MPC Page: 24
25 Analysis Controller In Manual Poor Performance in Auto Oscillations Random Deviations Externally Internally Cyclical Interaction Measurement Noise Disturbance Set Point Process Fast Disturbances Page: 25
26 Noise or Disturbance Noise is fast changes Disturbances are longer in duration Can coexist Noise can be filtered Page: 26
27 Analysis Controller In Manual Poor Performance in Auto Oscillations Random Deviations Externally Internally Cyclical Interaction Measurement Noise Disturbance Set Point Process Fast Disturbances Page: 27
28 Dead Band (Hysteresis) Dead band between CO and PV Appears like sluggish control action Can result in very incorrect tuning calculations Dead-band test: 2 steps + 1 in opposite direction Caustic Flow to Bleach Tower Dead Band 20% Dead Band Dead-Band Test Page: 28
29 Analysis Controller In Manual Poor Performance in Auto Oscillations Random Deviations Externally Internally Cyclical Interaction Measurement Noise Disturbance Set Point Process Fast Disturbances Page: 29
30 and Disturbance Rejection Previous comments about tuning apply (speed of response) has limits Disturbance rejection Settling time Limits mostly affected by dead time Other solutions are available Page: 30
31 Analysis Controller In Manual Poor Performance in Auto Oscillations Random Deviations Externally Internally Cyclical Interaction Measurement Noise Disturbance Set Point Process Fast Disturbances Page: 31
32 Control-Side Disturbances Go unnoticed until PV has been affected E.g. steam pressure to heat exchanger Change in Pressure = Disturbance Controller Output TC Temperature Controller Steam TT Heat Exchanger Process Flow Condensate Page: 32
33 Solution: Cascade Control Cascaded Inner Loop FT Controller Output TC Set Point Temperature Controller FC Cascaded Flow Controller Steam TT Heat Exchanger Disturbance Process Flow Condensate Cascade control isolates control-side disturbances, nonlinearities, valve problems Good practice for any slow control loop manipulating a flow Page: 33
34 Process-Side Disturbances Go unnoticed until PV has been affected E.g. process flow through heat exchanger Disturbance Page: 34
35 Solution: Feedforward Control Use disturbance to drive control action Feedforward control action can cancel out effect of major disturbances Feedforward Controller Disturbance Feedforward Controller FF FT Steam Heat Exchanger Feedback Controller TC TT Process Flow Condensate Page: 35
36 Summary Some poorly performing loops are a challenge to tune Realize that it might not be a tuning problem Do process tests to detect valve problems problems require repairs, not tuning Tune from step test data using rules or software Know the limitations of tuning The problem may originate from outside the loop Eliminate variability at its source Enhance the control strategy where needed Page: 36
37 OptiControls Inc. Houston, TX Ph: Questions? Page: 37
Control Loop Investigations
Control Loop Investigations Peter Thomas Control Specialists Ltd ExperTune User Conference Austin Texas April 2007 www.controlspecialists.co.uk Control Loop Investigations Bridging the gap between Industrial
More informationPrinciples and Practice of Automatic Process Control
Principles and Practice of Automatic Process Control Third Edition Carlos A. Smith, Ph.D., P.E. Department of Chemical Engineering University of South Florida Armando B. Corripio, Ph.D., P.E. Gordon A.
More informationEnhanced Single-Loop Control Strategies (Advanced Control) Cascade Control Time-Delay Compensation Inferential Control Selective and Override Control
Enhanced Single-Loop Control Strategies (Advanced Control) Cascade Control Time-Delay Compensation Inferential Control Selective and Override Control 1 Cascade Control A disadvantage of conventional feedback
More informationOpen Loop Tuning Rules
Open Loop Tuning Rules Based on approximate process models Process Reaction Curve: The process reaction curve is an approximate model of the process, assuming the process behaves as a first order plus
More informationChapter 7 Control. Part Classical Control. Mobile Robotics - Prof Alonzo Kelly, CMU RI
Chapter 7 Control 7.1 Classical Control Part 1 1 7.1 Classical Control Outline 7.1.1 Introduction 7.1.2 Virtual Spring Damper 7.1.3 Feedback Control 7.1.4 Model Referenced and Feedforward Control Summary
More informationFeedforward Control Feedforward Compensation
Feedforward Control Feedforward Compensation Compensation Feedforward Control Feedforward Control of a Heat Exchanger Implementation Issues Comments Nomenclature The inherent limitation of feedback control
More informationFeedback Control of Linear SISO systems. Process Dynamics and Control
Feedback Control of Linear SISO systems Process Dynamics and Control 1 Open-Loop Process The study of dynamics was limited to open-loop systems Observe process behavior as a result of specific input signals
More informationEnhanced Single-Loop Control Strategies Chapter 16
Enhanced Single-Loop Control Strategies Chapter 16 1. Cascade control 2. Time-delay compensation 3. Inferential control 4. Selective and override control 5. Nonlinear control 6. Adaptive control 1 Chapter
More informationCM 3310 Process Control, Spring Lecture 21
CM 331 Process Control, Spring 217 Instructor: Dr. om Co Lecture 21 (Back to Process Control opics ) General Control Configurations and Schemes. a) Basic Single-Input/Single-Output (SISO) Feedback Figure
More informationProcess Control & Design
458.308 Process Control & Design Lecture 5: Feedback Control System Jong Min Lee Chemical & Biomolecular Engineering Seoul National University 1 / 29 Feedback Control Scheme: The Continuous Blending Process.1
More informationValve Stiction - Definition, Modeling, Detection, Quantification and Compensation
Valve Stiction - Definition, Modeling, Detection, Quantification and Compensation Dr. M. A. A. Shoukat Choudhury Department of Chemical Engineering Bangladesh University of Engineering & Technology ()
More informationIndex Accumulation, 53 Accuracy: numerical integration, sensor, 383, Adaptive tuning: expert system, 528 gain scheduling, 518, 529, 709,
Accumulation, 53 Accuracy: numerical integration, 83-84 sensor, 383, 772-773 Adaptive tuning: expert system, 528 gain scheduling, 518, 529, 709, 715 input conversion, 519 reasons for, 512-517 relay auto-tuning,
More informationMulti-Input Multi-output (MIMO) Processes CBE495 LECTURE III CONTROL OF MULTI INPUT MULTI OUTPUT PROCESSES. Professor Dae Ryook Yang
Multi-Input Multi-output (MIMO) Processes CBE495 LECTURE III CONTROL OF MULTI INPUT MULTI OUTPUT PROCESSES Professor Dae Ryook Yang Fall 2013 Dept. of Chemical and Biological Engineering Korea University
More informationSystem Identification for Process Control: Recent Experiences and a Personal Outlook
System Identification for Process Control: Recent Experiences and a Personal Outlook Yucai Zhu Eindhoven University of Technology Eindhoven, The Netherlands and Tai-Ji Control Best, The Netherlands Contents
More informationSolutions for Tutorial 10 Stability Analysis
Solutions for Tutorial 1 Stability Analysis 1.1 In this question, you will analyze the series of three isothermal CSTR s show in Figure 1.1. The model for each reactor is the same at presented in Textbook
More informationSIMULATION SUITE CHEMCAD SOFTWARE PROCESS CONTROL SYSTEMS PROCESS CONTROL SYSTEMS COURSE WITH CHEMCAD MODELS. Application > Design > Adjustment
COURSE WITH CHEMCAD MODELS PROCESS CONTROL SYSTEMS Application > Design > Adjustment Based on F.G. Shinskey s 1967 Edition Presenter John Edwards P & I Design Ltd, UK Contact: jee@pidesign.co.uk COURSE
More informationControl Introduction. Gustaf Olsson IEA Lund University.
Control Introduction Gustaf Olsson IEA Lund University Gustaf.Olsson@iea.lth.se Lecture 3 Dec Nonlinear and linear systems Aeration, Growth rate, DO saturation Feedback control Cascade control Manipulated
More informationDynamics and PID control. Process dynamics
Dynamics and PID control Sigurd Skogestad Process dynamics Things take time Step response (response of output y to step in input u): k = Δy( )/ Δu process gain - process time constant (63%) - process time
More informationFundamental Principles of Process Control
Fundamental Principles of Process Control Motivation for Process Control Safety First: people, environment, equipment The Profit Motive: meeting final product specs minimizing waste production minimizing
More informationFinding the Root Cause Of Process Upsets
Finding the Root Cause Of Process Upsets George Buckbee, P.E. ExperTune, Inc. 2010 ExperTune, Inc. Page 1 Finding the Root Cause of Process Upsets George Buckbee, P.E., ExperTune Inc. 2010 ExperTune Inc
More informationIndex. INDEX_p /15/02 3:08 PM Page 765
INDEX_p.765-770 11/15/02 3:08 PM Page 765 Index N A Adaptive control, 144 Adiabatic reactors, 465 Algorithm, control, 5 All-pass factorization, 257 All-pass, frequency response, 225 Amplitude, 216 Amplitude
More informationAutomatic Control (TSRT15): Lecture 7
Automatic Control (TSRT15): Lecture 7 Tianshi Chen Division of Automatic Control Dept. of Electrical Engineering Email: tschen@isy.liu.se Phone: 13-282226 Office: B-house extrance 25-27 Outline 2 Feedforward
More information(Refer Slide Time: 1:42)
Control Engineering Prof. Madan Gopal Department of Electrical Engineering Indian Institute of Technology, Delhi Lecture - 21 Basic Principles of Feedback Control (Contd..) Friends, let me get started
More informationLecture 5 Classical Control Overview III. Dr. Radhakant Padhi Asst. Professor Dept. of Aerospace Engineering Indian Institute of Science - Bangalore
Lecture 5 Classical Control Overview III Dr. Radhakant Padhi Asst. Professor Dept. of Aerospace Engineering Indian Institute of Science - Bangalore A Fundamental Problem in Control Systems Poles of open
More informationProcess Solutions. Process Dynamics. The Fundamental Principle of Process Control. APC Techniques Dynamics 2-1. Page 2-1
Process Dynamics The Fundamental Principle of Process Control APC Techniques Dynamics 2-1 Page 2-1 Process Dynamics (1) All Processes are dynamic i.e. they change with time. If a plant were totally static
More informationInnovative Solutions from the Process Control Professionals
Control Station Innovative Solutions from the Process Control Professionals Software For Process Control Analysis, Tuning & Training Control Station Software For Process Control Analysis, Tuning & Training
More informationAli Karimpour Associate Professor Ferdowsi University of Mashhad
CONTROL IN INSTRUMENTATION Ali Karimpour Associate Professor Ferdowsi University of Mashhad Reference: - Advanced PID Control by Karl j. Astrom and Tore Hagglund, ISA, 2006 مدلسازی و کنترل صنعتی تالیف
More informationCONTROLLER PERFORMANCE ASSESSMENT IN SET POINT TRACKING AND REGULATORY CONTROL
ADCHEM 2, Pisa Italy June 14-16 th 2 CONTROLLER PERFORMANCE ASSESSMENT IN SET POINT TRACKING AND REGULATORY CONTROL N.F. Thornhill *, S.L. Shah + and B. Huang + * Department of Electronic and Electrical
More informationSubject: Introduction to Process Control. Week 01, Lectures 01 02, Spring Content
v CHEG 461 : Process Dynamics and Control Subject: Introduction to Process Control Week 01, Lectures 01 02, Spring 2014 Dr. Costas Kiparissides Content 1. Introduction to Process Dynamics and Control 2.
More informationREDUCING PROCESS VARIABLITY BY USING FASTER RESPONDING FLOWMETERS IN FLOW CONTROL
REDUCING PROCESS VARIABLITY BY USING FASTER RESPONDING FLOWMETERS IN FLOW CONTROL David Wiklund Marcos Peluso Sr. Principal Engineer Director of Temperature and Plantweb Development Rosemount, Inc. Rosemount,
More informationEE 422G - Signals and Systems Laboratory
EE 4G - Signals and Systems Laboratory Lab 9 PID Control Kevin D. Donohue Department of Electrical and Computer Engineering University of Kentucky Lexington, KY 40506 April, 04 Objectives: Identify the
More informationMetric prefixes and conversion constants
PID loop tuning This worksheet and all related files are licensed under the Creative Commons Attribution License, version 1.0. To view a copy of this license, visit http://creativecommons.org/licenses/by/1.0/,
More informationLaboratory Exercise 1 DC servo
Laboratory Exercise DC servo Per-Olof Källén ø 0,8 POWER SAT. OVL.RESET POS.RESET Moment Reference ø 0,5 ø 0,5 ø 0,5 ø 0,65 ø 0,65 Int ø 0,8 ø 0,8 Σ k Js + d ø 0,8 s ø 0 8 Off Off ø 0,8 Ext. Int. + x0,
More informationCHAPTER 15: FEEDFORWARD CONTROL
CHAPER 5: EEDORWARD CONROL When I complete this chapter, I want to be able to do the following. Identify situations for which feedforward is a good control enhancement Design feedforward control using
More informationBSc Project Fault Detection & Diagnosis in Control Valve
BSc Project Fault Detection & Diagnosis in Control Valve Supervisor: Dr. Nobakhti Content 2 What is fault? Why we detect fault in a control loop? What is Stiction? Comparing stiction with other faults
More informationAN INTRODUCTION TO THE CONTROL THEORY
Open-Loop controller An Open-Loop (OL) controller is characterized by no direct connection between the output of the system and its input; therefore external disturbance, non-linear dynamics and parameter
More informationIterative Feedback Tuning
Iterative Feedback Tuning Michel Gevers CESAME - UCL Louvain-la-Neuve Belgium Collaboration : H. Hjalmarsson, S. Gunnarsson, O. Lequin, E. Bosmans, L. Triest, M. Mossberg Outline Problem formulation Iterative
More informationTransient Stability Analysis with PowerWorld Simulator
Transient Stability Analysis with PowerWorld Simulator T1: Transient Stability Overview, Models and Relationships 2001 South First Street Champaign, Illinois 61820 +1 (217) 384.6330 support@powerworld.com
More informationB1-1. Closed-loop control. Chapter 1. Fundamentals of closed-loop control technology. Festo Didactic Process Control System
B1-1 Chapter 1 Fundamentals of closed-loop control technology B1-2 This chapter outlines the differences between closed-loop and openloop control and gives an introduction to closed-loop control technology.
More informationCHBE320 LECTURE XI CONTROLLER DESIGN AND PID CONTOLLER TUNING. Professor Dae Ryook Yang
CHBE320 LECTURE XI CONTROLLER DESIGN AND PID CONTOLLER TUNING Professor Dae Ryook Yang Spring 2018 Dept. of Chemical and Biological Engineering 11-1 Road Map of the Lecture XI Controller Design and PID
More informationControl System Design
ELEC ENG 4CL4: Control System Design Notes for Lecture #24 Wednesday, March 10, 2004 Dr. Ian C. Bruce Room: CRL-229 Phone ext.: 26984 Email: ibruce@mail.ece.mcmaster.ca Remedies We next turn to the question
More informationCompetences. The smart choice of Fluid Control Systems
Competences The smart choice of Fluid Control Systems Contents 1. Open-loop and closed-loop control Page 4 1.1. Function and sequence of an open-loop control system Page 4 1.2. Function and sequence of
More informationGuide to Selected Process Examples :ili3g eil;]iil
Guide to Selected Process Examples :ili3g eil;]iil Because of the strong interplay between process dynamics and control perfor mance, examples should begin with process equipment and operating conditions.
More informationDesign of Decentralised PI Controller using Model Reference Adaptive Control for Quadruple Tank Process
Design of Decentralised PI Controller using Model Reference Adaptive Control for Quadruple Tank Process D.Angeline Vijula #, Dr.N.Devarajan * # Electronics and Instrumentation Engineering Sri Ramakrishna
More informationCONTROL OF MULTIVARIABLE PROCESSES
Process plants ( or complex experiments) have many variables that must be controlled. The engineer must. Provide the needed sensors 2. Provide adequate manipulated variables 3. Decide how the CVs and MVs
More informationNonlinearControlofpHSystemforChangeOverTitrationCurve
D. SWATI et al., Nonlinear Control of ph System for Change Over Titration Curve, Chem. Biochem. Eng. Q. 19 (4) 341 349 (2005) 341 NonlinearControlofpHSystemforChangeOverTitrationCurve D. Swati, V. S. R.
More informationControl of MIMO processes. 1. Introduction. Control of MIMO processes. Control of Multiple-Input, Multiple Output (MIMO) Processes
Control of MIMO processes Control of Multiple-Input, Multiple Output (MIMO) Processes Statistical Process Control Feedforward and ratio control Cascade control Split range and selective control Control
More informationOverview of Control System Design
Overview of Control System Design General Requirements 1. Safety. It is imperative that industrial plants operate safely so as to promote the well-being of people and equipment within the plant and in
More informationProcess Unit Control System Design
Process Unit Control System Design 1. Introduction 2. Influence of process design 3. Control degrees of freedom 4. Selection of control system variables 5. Process safety Introduction Control system requirements»
More informationInternational Journal of Advance Engineering and Research Development PNEUMATIC CONTROL VALVE STICTION DETECTION AND QUANTIFICATION
Scientific Journal of Impact Factor (SJIF): 3.34 ISSN (Online): 2348-447 ISSN (Print) : 2348-646 International Journal of Advance Engineering and Research Development Volume 2, Issue 4, April -25 PNEUMATIC
More informationApplication Note #3413
Application Note #3413 Manual Tuning Methods Tuning the controller seems to be a difficult task to some users; however, after getting familiar with the theories and tricks behind it, one might find the
More informationControl System Design
ELEC ENG 4CL4: Control System Design Notes for Lecture #36 Dr. Ian C. Bruce Room: CRL-229 Phone ext.: 26984 Email: ibruce@mail.ece.mcmaster.ca Friday, April 4, 2003 3. Cascade Control Next we turn to an
More informationDESIGN AND DEVELOPMENT OF A PROCESS CONTROL VALVE DIAGNOSTIC SYSTEM BASED ON ARTIFICIAL NEURAL NETWORK ENSEMBLES
DESIGN AND DEVELOPMENT OF A PROCESS CONTROL VALVE DIAGNOSTIC SYSTEM BASED ON ARTIFICIAL NEURAL NETWORK ENSEMBLES by SUGITH SEWDASS Student Number: 20150121 A thesis submitted in fulfilment of the requirements
More informationIMPROVED TECHNIQUE OF MULTI-STAGE COMPENSATION. K. M. Yanev A. Obok Opok
IMPROVED TECHNIQUE OF MULTI-STAGE COMPENSATION K. M. Yanev A. Obok Opok Considering marginal control systems, a useful technique, contributing to the method of multi-stage compensation is suggested. A
More informationYTÜ Mechanical Engineering Department
YTÜ Mechanical Engineering Department Lecture of Special Laboratory of Machine Theory, System Dynamics and Control Division Coupled Tank 1 Level Control with using Feedforward PI Controller Lab Date: Lab
More informationAnalysis and Design of Control Systems in the Time Domain
Chapter 6 Analysis and Design of Control Systems in the Time Domain 6. Concepts of feedback control Given a system, we can classify it as an open loop or a closed loop depends on the usage of the feedback.
More informationDESIGN OF AN ON-LINE TITRATOR FOR NONLINEAR ph CONTROL
DESIGN OF AN ON-LINE TITRATOR FOR NONLINEAR CONTROL Alex D. Kalafatis Liuping Wang William R. Cluett AspenTech, Toronto, Canada School of Electrical & Computer Engineering, RMIT University, Melbourne,
More informationA Holistic Approach to the Application of Model Predictive Control to Batch Reactors
A Holistic Approach to the Application of Model Predictive Control to Batch Reactors A Singh*, P.G.R de Villiers**, P Rambalee***, G Gous J de Klerk, G Humphries * Lead Process Control Engineer, Anglo
More informationThis is an electronic reprint of the original article. This reprint may differ from the original in pagination and typographic detail.
Powered by TCPDF (www.tcpdf.org) This is an electronic reprint of the original article. This reprint may differ from the original in pagination and typographic detail. Jämsä-Jounela, Sirkka-Liisa; Poikonen,
More informationDesign and Tuning of Fractional-order PID Controllers for Time-delayed Processes
Design and Tuning of Fractional-order PID Controllers for Time-delayed Processes Emmanuel Edet Technology and Innovation Centre University of Strathclyde 99 George Street Glasgow, United Kingdom emmanuel.edet@strath.ac.uk
More informationChapter 2 Review of Linear and Nonlinear Controller Designs
Chapter 2 Review of Linear and Nonlinear Controller Designs This Chapter reviews several flight controller designs for unmanned rotorcraft. 1 Flight control systems have been proposed and tested on a wide
More informationAn Experimental Laboratory on Control Structures
An Experimental Laboratory on Control Structures Alberto Leva Abstract This paper presents a laboratory designed at the Politecnico di Milano, where different control structures can be experimented with
More informationIntroduction to. Process Control. Ahmet Palazoglu. Second Edition. Jose A. Romagnoli. CRC Press. Taylor & Francis Group. Taylor & Francis Group,
Introduction to Process Control Second Edition Jose A. Romagnoli Ahmet Palazoglu CRC Press Taylor & Francis Group Boca Raton London NewYork CRC Press is an imprint of the Taylor & Francis Group, an informa
More information24 volts (0.25 amps current-limited)
Question 1 Questions Suppose the lamp refuses to light up. A voltmeter registers 24 volts between test points C and D: A C E + 24 volts (0.25 amps current-limited) B D F First, list all the possible (single)
More informationExample on Root Locus Sketching and Control Design
Example on Root Locus Sketching and Control Design MCE44 - Spring 5 Dr. Richter April 25, 25 The following figure represents the system used for controlling the robotic manipulator of a Mars Rover. We
More informationLecture 7: Anti-windup and friction compensation
Lecture 7: Anti-windup and friction compensation Compensation for saturations (anti-windup) Friction models Friction compensation Material Lecture slides Course Outline Lecture 1-3 Lecture 2-6 Lecture
More informationOverview of Control System Design
Overview of Control System Design Introduction Degrees of Freedom for Process Control Selection of Controlled, Manipulated, and Measured Variables Process Safety and Process Control 1 General Requirements
More informationDesign and analysis of the prototype of boiler for steam pressure control
Design and analysis of the prototype of boiler for steam pressure control Akanksha Bhoursae, 2 Jalpa Shah, 3 Nishith Bhatt Institute of Technology, Nirma University, SG highway, Ahmedabad-38248,India 3
More informationSolution to exam in PEF3006 Process Control at Telemark University College
Solution to exam in PEF3006 Process Control at Telemark University College Exam date: 7. December 2015. Duration: 4 hours. Weight in final grade of the course: 100%. Teacher: Finn Aakre Haugen (finn.haugen@hit.no).
More informationIterative Controller Tuning Using Bode s Integrals
Iterative Controller Tuning Using Bode s Integrals A. Karimi, D. Garcia and R. Longchamp Laboratoire d automatique, École Polytechnique Fédérale de Lausanne (EPFL), 05 Lausanne, Switzerland. email: alireza.karimi@epfl.ch
More informationDYNAMIC SIMULATOR-BASED APC DESIGN FOR A NAPHTHA REDISTILLATION COLUMN
HUNGARIAN JOURNAL OF INDUSTRY AND CHEMISTRY Vol. 45(1) pp. 17 22 (2017) hjic.mk.uni-pannon.hu DOI: 10.1515/hjic-2017-0004 DYNAMIC SIMULATOR-BASED APC DESIGN FOR A NAPHTHA REDISTILLATION COLUMN LÁSZLÓ SZABÓ,
More informationDetermining Controller Constants to Satisfy Performance Specifications
Determining Controller Constants to Satisfy Performance This appendix presents a procedure for determining the tuning constants for feed back controllers that satisfy robust, time-domain performance specifications.
More informationAutomatic Control III (Reglerteknik III) fall Nonlinear systems, Part 3
Automatic Control III (Reglerteknik III) fall 20 4. Nonlinear systems, Part 3 (Chapter 4) Hans Norlander Systems and Control Department of Information Technology Uppsala University OSCILLATIONS AND DESCRIBING
More informationChapter 5 The SIMC Method for Smooth PID Controller Tuning
Chapter 5 The SIMC Method for Smooth PID Controller Tuning Sigurd Skogestad and Chriss Grimholt 5.1 Introduction Although the proportional-integral-derivative (PID) controller has only three parameters,
More informationProcess Control, 3P4 Assignment 6
Process Control, 3P4 Assignment 6 Kevin Dunn, kevin.dunn@mcmaster.ca Due date: 28 March 204 This assignment gives you practice with cascade control and feedforward control. Question [0 = 6 + 4] The outlet
More informationCascade Control of a Continuous Stirred Tank Reactor (CSTR)
Journal of Applied and Industrial Sciences, 213, 1 (4): 16-23, ISSN: 2328-4595 (PRINT), ISSN: 2328-469 (ONLINE) Research Article Cascade Control of a Continuous Stirred Tank Reactor (CSTR) 16 A. O. Ahmed
More informationBuilding knowledge from plant operating data for process improvement. applications
Building knowledge from plant operating data for process improvement applications Ramasamy, M., Zabiri, H., Lemma, T. D., Totok, R. B., and Osman, M. Chemical Engineering Department, Universiti Teknologi
More informationCBE495 LECTURE IV MODEL PREDICTIVE CONTROL
What is Model Predictive Control (MPC)? CBE495 LECTURE IV MODEL PREDICTIVE CONTROL Professor Dae Ryook Yang Fall 2013 Dept. of Chemical and Biological Engineering Korea University * Some parts are from
More informationCHAPTER 10: STABILITY &TUNING
When I complete this chapter, I want to be able to do the following. Determine the stability of a process without control Determine the stability of a closed-loop feedback control system Use these approaches
More informationIntroduction to System Identification and Adaptive Control
Introduction to System Identification and Adaptive Control A. Khaki Sedigh Control Systems Group Faculty of Electrical and Computer Engineering K. N. Toosi University of Technology May 2009 Introduction
More informationOptimizing Coagulation with Streaming Current Plant Operations Conference Presented by the VA AWWA Plant Operations Committee
Optimizing Coagulation with Streaming Current 2016 Plant Operations Conference Presented by the VA AWWA Plant Operations Committee Outline Coagulation Background Benefits of SCMs Theory of Operation System
More informationAnalysis and Synthesis of Single-Input Single-Output Control Systems
Lino Guzzella Analysis and Synthesis of Single-Input Single-Output Control Systems l+kja» \Uja>)W2(ja»\ um Contents 1 Definitions and Problem Formulations 1 1.1 Introduction 1 1.2 Definitions 1 1.2.1 Systems
More informationDr Ian R. Manchester Dr Ian R. Manchester AMME 3500 : Root Locus
Week Content Notes 1 Introduction 2 Frequency Domain Modelling 3 Transient Performance and the s-plane 4 Block Diagrams 5 Feedback System Characteristics Assign 1 Due 6 Root Locus 7 Root Locus 2 Assign
More informationSystems Engineering/Process Control L1
Systems Engineering/Process Control L1 What is Systems Engineering/Process Control? Graphical system representations Fundamental control principles Reading: Systems Engineering and Process Control: 1.1
More informationMASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Mechanical Engineering 2.04A Systems and Controls Spring 2013
MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Mechanical Engineering 2.04A Systems and Controls Spring 2013 Problem Set #4 Posted: Thursday, Mar. 7, 13 Due: Thursday, Mar. 14, 13 1. Sketch the Root
More informationExam. 135 minutes + 15 minutes reading time
Exam January 23, 27 Control Systems I (5-59-L) Prof. Emilio Frazzoli Exam Exam Duration: 35 minutes + 5 minutes reading time Number of Problems: 45 Number of Points: 53 Permitted aids: Important: 4 pages
More informationResearch Article. World Journal of Engineering Research and Technology WJERT.
wjert, 2015, Vol. 1, Issue 1, 27-36 Research Article ISSN 2454-695X WJERT www.wjert.org COMPENSATOR TUNING FOR DISTURBANCE REJECTION ASSOCIATED WITH DELAYED DOUBLE INTEGRATING PROCESSES, PART I: FEEDBACK
More information3.1 Overview 3.2 Process and control-loop interactions
3. Multivariable 3.1 Overview 3.2 and control-loop interactions 3.2.1 Interaction analysis 3.2.2 Closed-loop stability 3.3 Decoupling control 3.3.1 Basic design principle 3.3.2 Complete decoupling 3.3.3
More informationAdvanced control If single-loop feedback control (PID) alone is not good enough
The decision hierarchy is based on time scale separation setpoints Advanced control (MPC) setpoints Fast regulatory control (PID) PROCESS Advanced control If single-loop feedback control (PID) alone is
More informationUsing the PI System in Chemical Engineering Education and Research
Using the PI System in Chemical Engineering Education and Research Željka Ujević Andrijić & Nenad Bolf University of Zagreb, Faculty of Chem.Eng. & Tech., Croatia 1 Content University of Zagreb, Faculty
More informationAcknowledgements. Control System. Tracking. CS122A: Embedded System Design 4/24/2007. A Simple Introduction to Embedded Control Systems (PID Control)
Acknowledgements A Simple Introduction to Embedded Control Systems (PID Control) The material in this lecture is adapted from: F. Vahid and T. Givargis, Embedded System Design A Unified Hardware/Software
More informationControl 2. Proportional and Integral control
Control 2 Proportional and Integral control 1 Disturbance rejection in Proportional Control Θ i =5 + _ Controller K P =20 Motor K=2.45 Θ o Consider first the case where the motor steadystate gain = 2.45
More informationSVSM User Training and Example Sequences
Application Note 1500-012 SVSM User Training and Example Sequences SQUID VSM Training Example 1 Zero Field Cooled Field Cooled Measurement of a Superconducting Sample This sequence and data file demonstrates
More informationA unified double-loop multi-scale control strategy for NMP integrating-unstable systems
Home Search Collections Journals About Contact us My IOPscience A unified double-loop multi-scale control strategy for NMP integrating-unstable systems This content has been downloaded from IOPscience.
More informationDistributed Parameter Systems
Distributed Parameter Systems Introduction All the apparatus dynamic experiments in the laboratory exhibit the effect known as "minimum phase dynamics". Process control loops are often based on simulations
More informationImproved cascade control structure for enhanced performance
Improved cascade control structure for enhanced performance Article (Unspecified) Kaya, İbrahim, Tan, Nusret and Atherton, Derek P. (7) Improved cascade control structure for enhanced performance. Journal
More informationRELAY AUTOTUNING OF MULTIVARIABLE SYSTEMS: APPLICATION TO AN EXPERIMENTAL PILOT-SCALE DISTILLATION COLUMN
Copyright 2002 IFAC 15th Triennial World Congress, Barcelona, Spain RELAY AUTOTUNING OF MULTIVARIABLE SYSTEMS: APPLICATION TO AN EXPERIMENTAL PILOT-SCALE DISTILLATION COLUMN G. Marchetti 1, C. Scali 1,
More informationCHAPTER 13: FEEDBACK PERFORMANCE
When I complete this chapter, I want to be able to do the following. Apply two methods for evaluating control performance: simulation and frequency response Apply general guidelines for the effect of -
More informationSystems Engineering/Process Control L1
Systems Engineering/Process Control L1 What is Systems Engineering/Process Control? Graphical system representations Fundamental control principles Reading: Systems Engineering and Process Control: 1.1
More informationIdentification of ARX, OE, FIR models with the least squares method
Identification of ARX, OE, FIR models with the least squares method CHEM-E7145 Advanced Process Control Methods Lecture 2 Contents Identification of ARX model with the least squares minimizing the equation
More information